Package org.encog.ml.svm.training

Examples of org.encog.ml.svm.training.SVMTrain.iteration()

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  private SVM create()
  {
    MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    SVM result = new SVM(2,SVMType.EpsilonSupportVectorRegression,KernelType.RadialBasisFunction);
    final SVMTrain train = new SVMTrain(result, training);
    train.iteration();
    return result;
  }
 
  public void testPersistEG()
  {
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  }
 
  public void train(SVM network,MLDataSet training)
  {
    final SVMTrain train = new SVMTrain(network, training);
    train.iteration();
  }
 
  public void predict(SVM network)
  {
    NumberFormat f = NumberFormat.getNumberInstance();
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      double g = dialog.getGamma().getValue();
      SVM method = (SVM) file.getObject();
      SVMTrain train = new SVMTrain((SVM) method, trainingData);
      train.setC(c);
      train.setGamma(g);
      train.iteration();
      double error = method.calculateError(trainingData);
      if (EncogWorkBench.askQuestion("Training Done",
          "Error: " + Format.formatPercent(error)
              + "\nSave training?")) {
        file.save();
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  }
 
  public void train(SVM network,MLDataSet training)
  {
    final SVMTrain train = new SVMTrain(network, training);
    train.iteration();
  }
 
  public void predict(SVM network)
  {
    NumberFormat f = NumberFormat.getNumberInstance();
View Full Code Here
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  private SVM create()
  {
    MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    SVM result = new SVM(2,SVMType.EpsilonSupportVectorRegression,KernelType.RadialBasisFunction);
    final SVMTrain train = new SVMTrain(result, training);
    train.iteration();
    return result;
  }
 
  public void testPersistEG()
  {
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